Historical urban potential street vector identification method based on axis extraction

Abstract

The invention provides a historical urban potential street vector identification method based on axis extraction. Comprising the following steps: step 1, acquiring and processing historical urban map data; step 2, division and preliminary evaluation of calculation units; 3, street axis extraction based on a triangular mesh and a parameter rule; and 4, optimizing and outputting the street axis. According to the method, the potential street axes with passing conditions in the historical urban area can be mined by setting rules for the existing planning framework and street layout, and the generated results are screened and guided according to the parameter rules. And quantitative data support is provided for a designer to optimize a street network structure on the premise that the existing texture of a historical urban area is not damaged.

Description

Technical Field

[0001] The present invention relates to the fields of computer-aided design, geographic information systems, alley weaving technology, and urban renewal, and particularly relates to a method for identifying potential alley vectors in historical urban areas based on axis extraction. Background Art

[0002] Urban development has shifted from incremental expansion to mainly stock renewal. There are generally problems of low density and continuity of the alley network in the old urban areas of cities. Therefore, improving the alley network system in the old city is one of the key tasks in urban renewal.

[0003] The historical urban area is a piece of land in the city with the inheritance of historical culture and a high degree of functional concentration. Its alley system reflects the characteristics of traditional texture and at the same time bears the main commuting needs of residents. Due to the influence of factors such as the protection of historical and cultural elements, the boundaries of property plots, the blocks to be retained in the current situation, and rivers, problems such as uneven alley density, too narrow road widths, and many dead-end roads are particularly obvious in the historical urban area. In previous historical urban area renewal practices, designers needed to conduct on-site investigations and data collection to obtain the current situation of the site, and rely on experience to identify potential alleys with passable conditions in the complex current situation site, and finally optimize the alley system. With the progress of big data and artificial intelligence technologies, new methods and tools have been provided for identifying potential alleys in historical urban areas. The existing alley axis recognition technologies mainly include two methods based on vector data and image data. Among them,

[0004] The method based on vector data relies on clear geometric and topological structures and can perform precise analysis and optimization, but it has a large dependence on high-quality and multi-source site data and is difficult to cope with the situation of insufficient data.

[0005] The method based on image data uses remote sensing images and deep learning technologies and performs well in complex backgrounds and large-scale scenes, but the controllability and interpretability of the generated results are weak, and the need for a large amount of labeled data increases the model training cost.

[0006] These methods still have limitations in the optimization of existing complex built historical urban area alleys.

[0007] In view of the above technical problems, the present invention proposes a method for identifying potential alley vectors in historical urban areas based on axis extraction. The present invention sets rules for the existing planning framework and alley layout, discovers potential alley axes with passable conditions in the historical urban area, and screens according to parameter rules to guide the generated results. Without destroying the existing texture of the historical urban area, it provides quantitative data support for designers to optimize the alley network structure.

[0008] In order to achieve the above technical purpose, the present invention adopts the following technical solutions:

[0009] A method for identifying potential street vectors in historical urban areas based on axis extraction, comprising the following steps:

[0010] A. Obtain the map data of the historical block and determine the spatial element map;

[0011] B. Preliminary division and preliminary evaluation of the street network map;

[0012] Based on the block zoning theory, divide the spatial element map determined in step A into independent calculation units; according to the preset evaluation indicators, conduct a preliminary evaluation of each calculation unit, and screen out the calculation units that meet the conditions to be optimized;

[0013] C. Generate street axes for the calculation units to be optimized screened out in step B based on parameter rules;

[0014] D. Conduct visual interaction and evaluation on the street axes generated in step C.

[0015] Beneficial effects: For the identification of street networks in urban renewal, the present invention mainly focuses on the existing streets in historical urban areas, explores potential streets that can improve traffic conditions, rather than regenerating a regular street network system, and proposes a calculation and evaluation method based on multiple evaluation indicators, providing quantitative data support for designers, and significantly improving the scientificity and operability of the street network generation and optimization process. Through the comprehensive evaluation of multiple indicators and the setting of preset conditions, not only can the calculation units to be optimized be accurately identified, but also effective monitoring and evaluation means for the actual performance after the street network transformation are provided. Finally, by visually interacting with and viewing the generated street axes, designers can adjust the street axis plan in real time, manually delete or add important axes, improving the display and interactivity of street axis identification.

[0016] In an optional implementation manner, step B specifically includes the following sub-steps:

[0017] B1. Use the calculation units defined based on the block zoning theory as the basic units for street network generation and optimization; the calculation units have a set of spatial attributes specifically for street network generation, and are divided and evaluated at the block scale; during the optimization process, the calculation units are regarded as the genes of the genetic algorithm, enabling the street network generation to proceed according to a unified rule logic in a parallel computing framework;

[0018] B2. Define the calculation unit as a plot formed by the closure of main road - main road, main road - branch road, and branch road - branch road. Obtain or update the current graphic data structure from the street network map object. The calculation unit includes at least 3 nodes to form a closed plot. Traverse the graph structure through the depth - first search algorithm to identify the closed plot for creating the initial calculation unit, or custom - select the calculation unit. The calculation unit, as the basic unit for optimization calculation, is added to the street network map structure.

[0019] B3. Conduct a preliminary evaluation of the street network indicators for each calculation unit.

[0020] Beneficial effects: The present invention proposes a calculation and evaluation method based on multiple evaluation indicators, providing quantitative data support for designers, and significantly improving the scientificity and operability of the street network generation and optimization process. Through the comprehensive evaluation of multiple indicators and the setting of preset conditions, not only can the calculation units that need to be optimized be accurately identified, but also effective monitoring and evaluation means for the actual performance after the street network transformation are provided.

[0021] In an alternative embodiment, in step B, the preset evaluation indicators include:

[0022] Street density, street proportion, and plot uniformity. Among them, the global street density reaches 8.0 km / km², the street proportion is 0.5 - 0.7, and the plot uniformity is 1:2 - 2:3. When the indicators of a certain calculation unit do not meet the above preset conditions, it is determined that the calculation unit needs further optimization.

[0023] Beneficial effects: In step B of the present invention, through the preset of multiple quantitative evaluation indicators such as street density, street proportion, and plot uniformity, the comprehensive evaluation of the calculation unit is realized. This method can accurately identify the areas that do not meet the optimization standards in the overall street network generation process and provide quantitative data support, providing a scientific basis for designers' decision - making.

[0024] In an alternative embodiment, the generation of street axes based on parameter rules in step C specifically includes the following sub - steps:

[0025] C1. Determine the current elements according to the existing map attributes and parameter rules, specifically including the following sub - steps:

[0026] C11. Street - level analysis: Analyze the street levels, determine the standard width d of various streets, and offset the center line of the existing streets outward by d / 2 to define part of the non - street area 1.

[0027] C12. Analysis of building types: Analyze the coordinates and types of buildings in the map. Determine the outward offset distances of ordinary building blocks and historical building blocks as n1 + d / 2 and n2 + d / 2 respectively according to the building setback distance n1, the historical building setback distance n2, and the standard width d of the street. Define the part of the non-street area 2 according to the extended coordinates.

[0028] C13. Analysis of area types: Analyze the coordinates and types of each special area in the map. Define the part of the non-street area 3. Special areas include areas such as water systems, green spaces, boundaries of closed communities, and property plots that cannot be used for street weaving and mending. Determine the sum of the non-street area 1, non-street area 2, and non-street area 3, that is, all areas in the map format except for streets and non-renewable areas, as the blank area.

[0029] C2. Use constrained Delaunay triangulation to perform grid division on the blank area. Select the largest continuous blank area as the basic convex polygon for generating the triangular grid. Set the constraints and quality parameters of the triangular grid, and execute the CDT process to generate the triangular grid.

[0030] C3. If a triangular grid shares an edge with another triangular grid, define this edge as an adjacent edge. Connect the midpoints of pairwise adjacent edges and extend the connection line to the inner and outer boundaries of the convex polygon to obtain the preliminary street axis.

[0031] C4. Use steps C1 - C3 to extract all generated preliminary street axes and cache the results for repeated use and screening.

[0032] C5. Construct a filter to screen the generated preliminary street axes through parameter rules to ensure that the final street skeleton structure meets the requirements of geometric logic and road design specifications.

[0033] C6. Extract intersections, split street axes, and simplify and deduplicate the whole for the refined street axes obtained after screening. The street axes can be connected to the existing street network and are restricted within the calculation unit according to parameter rules, summarizing the street network topological structure without dead ends. The final street axes retain single - street information and the overall graph structure, providing a data basis for subsequent optimization calculations.

[0034] Beneficial effects: Through the analysis of street levels and area types, the present invention ensures that the axis generation conforms to spatial constraints; optimizes the division of the blank area based on constrained Delaunay triangular grids to improve calculation stability; combines parameter rules for screening to ensure that the street skeleton structure conforms to geometric logic and road design specifications; finally, through intersection extraction, axis splitting, and topological optimization, a complete street network without dead ends is generated, seamlessly connected to the existing street structure, providing a high - quality data basis for subsequent optimization calculations.

[0035] In an alternative embodiment, in step C5, the following screening rules are satisfied simultaneously:

[0036] Screening rule 1. Length and shape of streets and alleys: The Douglas-Peucker algorithm compresses redundant path control points to obtain the simplified shape of streets and alleys; calculate the length ratio of streets and alleys. Given the length of the street and alley between two points as D and the length of the line connecting the two points as d, the length ratio is r = D / d, and this ratio is between 1 and 1.5;

[0037] Screening rule 2. Angle between streets and alleys: Determine whether the angles formed by the given path with the streets and alleys at the starting point or the ending point meet the requirements. Specifically, calculate the angle between the vector from the starting point of the path to the next turning point and the normal vector of the side where the starting point is located. If these angles are between 45 degrees and 90 degrees, the angle is considered appropriate and meets the requirements; if these angles are less than 45 degrees, the angle is considered too small and does not meet the requirements; the same applies to the ending point of the path;

[0038] Screening rule 3. Analysis of intersections: Analyze the type of intersections, and determine the intersection threshold r according to the road design specifications; define whether a street and alley can be generated at a certain position by expanding a circular area with a radius of the intersection threshold r centered on the existing arterial road and branch road nodes. For the generation of street and alley-level road networks, r is set to 50m.

[0039] Beneficial effects: The present invention optimizes the street and alley network through screening rules, simplifies the shape of streets and alleys, ensures the rationality of path design, standardizes the generation of intersections, provides scientific data support for designers, and improves the efficiency and accuracy of optimizing the street and alley network.

[0040] In an alternative embodiment, step A specifically includes the following sub-steps:

[0041] A1. Obtain map data from an open-source platform, determine the street and alley recognition range, classify the layers of the selected DXF file of the historical block texture, extract the plot boundary, street and alley centerline, and building contour information, and convert it into the Map file format;

[0042] A2. Use the NetTopologySuite library in the Unity engine to read the imported DXF file, classify the geometric data therein, and convert it into the MAP file format;

[0043] A3. Define a class named Map, which contains multiple properties and collections for storing and managing different elements in the map. Each Map object includes the following content: Map name: used to identify a specific map;

[0044] Road list: includes roads at different levels, and stores arterial roads, branch roads, and streets and alleys respectively;

[0045] List of buildings: includes ordinary buildings and historical buildings, marked as non-traversable areas;

[0046] List of areas: stores non-traversable enclosed areas and water systems.

[0047] Beneficial effects: The present invention obtains map data from an open-source platform through big data, extracts key information, and performs data classification processing, solving the problem of large workload in collecting data by traditional manual methods and improving design efficiency.

[0048] In an alternative embodiment, in step A2, primitive deduplication and line data conversion are performed before import to ensure the smooth progress of subsequent calculation and analysis processes.

[0049] Beneficial effects: The present invention uses the NetTopologySuite library in the Unity engine to read the imported DXF file, classify the geometric data therein and convert it into the MAP file format; primitive deduplication and line data conversion are performed before import to ensure the smooth progress of subsequent calculation and analysis processes.

[0050] In summary, the present invention sets rules for the existing planning framework and street layout, discovers potential street axes with traffic conditions in the historical urban area, and screens according to parameter rules to guide the generated results. Without damaging the existing texture of the historical urban area, it provides quantitative data support for designers to optimize the street network structure. Brief Description of the Drawings

[0051] Figure 1 Flowchart of the method of the present invention;

[0052] Figure 2 Ideal model - triangular mesh diagram;

[0053] Figure 3 Ideal model - street axis identification diagram;

[0054] Figure 4 Ideal model - comparison diagram of program extraction results and manual design results;

[0055] Figure 5 Current situation model - triangular mesh diagram;

[0056] Figure 6 Current situation model - street axis identification diagram;

[0057] Figure 7 Current situation model - comparison diagram of program extraction results and manual design results. Detailed Description of the Invention

[0058] The following further illustrates the technical solution of the present invention in conjunction with the accompanying drawings of the specification and specific implementation cases, so that those skilled in the art can better understand the present invention and be able to implement it. However, the cited implementation cases are not intended to limit the present invention.

[0059] The present invention proposes a method for identifying potential street and alley vectors in historical urban areas based on axis extraction. The specific method process is as Figure 1 shown. First, obtain urban block data, import it into the Unity platform to establish a spatial element map, and conduct a preliminary evaluation.

[0060] Then generate street and alley axes through triangular meshes, and combine existing conditions and planning requirements, such as street and alley widths, building positions, etc., to screen out suitable street and alley axes;

[0061] Finally, conduct a visual display in the Unity platform and output the final street and alley axis plan, which specifically includes the following steps:

[0062] 1. Obtain relevant data of the urban map, identify basic elements, and establish a block street and alley network file.

[0063] 1.1. First, through the map import function, import open source platform or existing geographic information data (such as DXF format files) into the system, and identify and classify the basic elements within the block. The main elements to be identified include: street and alley levels, building types, water systems, and closed areas. All classified data will be stored in the map management module and used as the basic data for street and alley network generation and analysis.

[0064] 1.2. Read the imported DXF file through the NetTopologySuite library in the Unity engine, conduct overlay analysis and geometric data conversion on each data layer, street and alley, building, and water system data, generate a complete MAP file format block street and alley network map, and save it for future use. When opening the same map for the second time, you can click the load map option.

[0065] 1.3. In the map management - basic information, you can view the detailed geometric information within the block, including: ① Map name: used to identify the specific map; ② Road list: classified according to different street and alley levels, specifically including main roads, branch roads, and streets and alleys. Through the graphical interface, users can clearly see the geometric information of each street and alley and its relative position in the overall block; ③ Building list: including detailed information of ordinary buildings and historical buildings; ④ Area information: identifying water systems and other non-traversable closed areas. The positions of the basic elements in the map can be viewed with reference to the legend.

[0066] Preferably, in step 1.2, primitive deduplication and line data conversion are performed before import to ensure the smooth progress of subsequent calculation and analysis processes.

[0067] 2. Establish blocks by clicking on the nodes of the street network, divide the street network units, and generate Delaunay triangular meshes.

[0068] 2.1. Click on Map Management - Create Block, set the nodes of the street network, and each block unit is connected by these nodes. Users can click through the program interface to divide the block range and save it for future use.

[0069] 2.2. Click on Create Navigation Block - Create All Calculation Units. The entire map is divided into independent calculation units based on the block zoning theory, and clicking on a certain calculation unit can perform a preliminary evaluation.

[0070] 2.3. Click on Evaluation, check the evaluation indicators that need to be calculated, and the system will calculate the basic evaluation scores of each block unit. The evaluation indicators include the connectivity of the streets, the mobility of the streets, the rationality of the plot division, etc. This evaluation score will be used as a reference standard for the subsequent optimization stage.

[0071] 3. Generate the basic street axes and screen them according to the parameter rules.

[0072] 3.1. Click on Create Triangular Mesh. Based on each street network unit within the block, generate a triangular mesh that conforms to the actual situation according to the property boundaries, preserved buildings, existing streets, water systems, and other non-traversable areas. Obtain the ideal model - triangular mesh diagram as shown in Figure 2 Figure. Through this triangular mesh, the system will automatically extract all possible preliminary street axes.

[0073] 3.2 According to the rigid rules of the street network in urban design, the program formulates corresponding geometric parameters to screen higher-quality street axes, such as controlling the distance and angle between two axes, controlling the distance between the axis endpoints and the existing streets, and merging similar axes. Users can manually modify the screening parameters to generate a more ideal axis result.

[0074] 3.3. Click on Split Axis to split the preliminarily screened streets and add them to the street dataset to be optimized, display all possible screened street axes, and obtain the ideal model - street axis identification diagram as shown in Figure 3 Figure.

[0075] As a further preference for the technical solution of the present invention, in step C5, the following screening rules are simultaneously satisfied:

[0076] Screening Rule 1. Length and shape of streets and alleys: The Douglas-Peucker algorithm compresses redundant path control points to obtain the simplified shape of streets and alleys; calculates the length ratio of streets and alleys. Given the length of the street and alley between two points as D and the length of the line connecting the two points as d, the length ratio is r = D / d, and this ratio is between 1 and 1.5;

[0077] Screening Rule 2. Angle between streets and alleys: Determine whether the angle formed by the given path and the street or alley due to the starting or ending point meets the requirements. Specifically, calculate the angle between the vector from the path starting point to the next turning point and the normal vector of the side where the starting point is located. If these angles are between 45 degrees and 90 degrees, the angle is considered appropriate and meets the requirements; if these angles are less than 45 degrees, the angle is considered too small and does not meet the requirements; the same applies to the path ending point;

[0078] Screening Rule 3. Analysis of intersections: Analyze the intersection type and determine the intersection threshold r according to the road design specifications; define whether a street and alley can be generated at this location by expanding a circular area with a radius of the intersection threshold r centered on the existing main road and branch road nodes. For the generation of street and alley-level road networks, r is set to 50m.

[0079] 4. Visualize and optimize the preliminary screened street and alley axes in the Unity engine.

[0080] 4.1 The street and alley axes can be optimized in real time by interactively viewing and editing the imported street and alley axes.

[0081] 4.2 After completing the optimization process, the system will output the finally optimized potential street and alley axes. The final street and alley axis results can be exported in DXF format for actual urban planning and architectural design. Figure 4 The figure shown on the left is the final result diagram extracted by the program, Figure 4 The figure shown on the right is the result diagram of manual design. The program extraction method retains the overall structure control of manual design, divides the land parcels regularly, and at the same time has stronger adaptability at the detail level, can automatically detect and retain the small-scale street and alley forms, and is applicable to the micro-scale optimization of complex blocks.

[0082] The following combines Figures 5 - 7 , taking a certain historical block area in He County, Ma'anshan City, Anhui Province as an example, to further illustrate the method of this example:

[0083] 1. Obtain data related to the urban map, identify the basic elements, and establish a street and lane network file. The function of this area is defined as a historical block. Import the vector data of the reference case prototype plan drawing into the AutoCAD software platform, classify the data according to layers, convert it into polylines, and store it in DXF format. Import the DXF into the Unity platform, identify the street and lane levels and current elements, and establish a street and lane network MAP file.

[0084] 2. Establish blocks by clicking on the nodes of the street and lane network, divide the street and lane network units, and generate Delaunay triangular meshes. Divide the plots in this area based on the criteria of arterial road - arterial road, arterial road - branch road, and branch road - branch road. Click to create blocks, check the evaluation indicators that need to be calculated, and finally form 11 calculation units, that is, 11 plots, as Figure 5 shown in the current situation model - triangular mesh diagram.

[0085] 3. Generate the basic street and lane axes and conduct preliminary screening according to the parameter rules. Generate the street and lane axes according to the geometric screening factors and default screening parameters, and divide the screened streets and lanes for all plots. Obtain the Figure 6 shown in the current situation model - street and lane axis identification diagram.

[0086] 4. Visually display the preliminarily screened street and lane axes in the Unity engine, and optimize the street and lane axes in real time by interactively viewing and editing the imported street and lane axes. After completing the optimization process, the system will output the finally optimized potential street and lane axes. The final street and lane axis results can be exported in DXF format for actual urban planning and architectural design.

[0087] Figure 7 Shown is the comparison diagram between the final program extraction result and the manual design result.

Claims

1. A method for identifying potential street vectors in historical urban areas based on axis extraction, characterized in that, It includes the following steps: A. Obtain the map data of the historical block and determine the spatial element map; B. Preliminary division and preliminary evaluation of the street network map; Based on the block zoning theory, divide the spatial element map determined in step A into independent calculation units; according to the preset evaluation indicators, conduct a preliminary evaluation of each calculation unit, and screen out the calculation units that meet the conditions to be optimized; C. Generate street axes for the calculation units to be optimized screened out in step B based on parameter rules; D. Conduct visual interaction and evaluation on the street axes generated in step C.

2. The method for identifying potential street vectors in historical urban areas based on axis extraction according to claim 1, characterized in that Step B specifically includes the following sub-steps: B1. Use the calculation unit defined based on the block zoning theory as the basic unit for street network generation and optimization; the calculation unit has a set of spatial attributes specifically for street network generation, and is divided and evaluated at the block scale; in the subsequent optimization process, the calculation unit enables the street network generation to be carried out according to a unified rule logic in a parallel computing framework; B2. Define the calculation unit as a plot closed by the main and secondary arterial roads, obtain or update the current graphic data structure from the street network map object, and the calculation unit includes at least 3 nodes to form a closed plot; traverse the graph structure through the depth-first search algorithm to identify the closed plot for creating the initial calculation unit, or customize the selection of the calculation unit, and the calculation unit is added to the street network map structure as the basic unit for optimization calculation; B3. Conduct a preliminary evaluation of the street network indicators for each calculation unit.

3. The method for identifying potential street vector in a historical urban area based on axis extraction according to claim 1, wherein In step B, the preset evaluation indicators include: Street density, street proportion, and plot uniformity. Among them, the global street density reaches 8.0 km / km², the street proportion is 0.5 - 0.7, and the plot uniformity is 1:2 - 2:

3. When the indicators of a certain calculation unit do not meet the above preset conditions, it is determined that the calculation unit needs further optimization.

4. The method for identifying potential street vector in historical urban area based on axis extraction according to claim 1, wherein The generation of street axes based on parameter rules in step C specifically includes the following sub-steps: C1. Determine the current situation elements according to the existing map attributes and parameter rules, specifically including the following sub-steps: C11. Street level analysis: Analyze the street levels, determine the standard width d of various streets, and offset the center line of the existing streets outward by d / 2 to define part of the non-street area 1; C12. Building type analysis: Analyze the coordinates and types of buildings in the map, and determine the outward offset distances of ordinary building and historical building blocks as n1 + d / 2 and n2 + d / 2 respectively according to the building setback distance n1, the historical building setback distance n2, and the standard width d of the street. Define part of the non-street area 2 according to the extended coordinates; C13. Area type analysis: Analyze the coordinates and types of each special area in the map, define part of the non-street area 3, and special areas include areas such as water systems, green spaces, closed community boundaries, and property plots that cannot be used for street patching; determine the sum of the non-street area 1, the non-street area 2, and the non-street area 3, that is, all areas in the map except for streets and non-transformable areas, as the blank area; C2. Use constrained Delaunay triangulation to perform mesh division on the blank area, select the largest continuous blank area as the basic convex polygon for generating the triangular mesh, set the constraints and quality parameters of the triangular mesh, execute the CDT process, and generate the triangular mesh; C3. If a triangular mesh shares an edge with another triangular mesh, define this edge as an adjacent edge, connect the midpoints of pairwise adjacent edges, and extend the connection line to the inner and outer boundaries of the convex polygon to obtain the preliminary street and alley axes; C4. Use steps C1 - C3 to extract all the generated preliminary street and alley axes, and cache the results for repeated use and screening; C5. Construct a filter, and screen the generated preliminary street and alley axes through parameter rules to ensure that the final street and alley skeleton structure meets the requirements of geometric logic and road design specifications; C6. Extract the intersection points, split the street and alley axes, and simplify and de - duplicate them as a whole for the refined street and alley axes obtained after screening. The street and alley axes can be connected to the existing street and alley network and are restricted within the calculation unit according to the parameter rules, and a street and alley network topological structure without dead - end roads is generalized; the final street and alley axes retain the single - street and alley information and the overall graph structure, providing a data basis for subsequent optimization calculations.

5. The method for identifying potential alley vectors in historical urban areas based on axis extraction according to claim 4, characterized in that In step C5, the following screening rules are simultaneously satisfied: Screening rule 1. The length and shape of the street and alley: The Douglas - Peucker algorithm compresses the redundant path control points to obtain the simplified shape of the street and alley; calculate the length ratio of the street and alley. Given the length of the street and alley between two points as D and the length of the straight - line connection between the two points as d, the length ratio is r = D / d, and this ratio is between 1 - 1.5; Screening rule 2. The angle of the street and alley: Determine whether the angles formed by the given path with the street and alley at the starting or ending point meet the requirements. Specifically, calculate the angle between the vector from the path starting point to the next turning point and the normal vector of the side where the starting point is located. If these angles are between 45 degrees and 90 degrees, it is considered that the angle is appropriate and meets the requirements; if these angles are less than 45 degrees, it is considered that the angle is too small and does not meet the requirements; Screening rule 3. Analysis of intersection points: Analyze the intersection point type, and determine the intersection threshold r according to the road design specifications; use the existing main road and branch road nodes as the center, and define whether a street and alley can be generated at this location by expanding a circular area with a radius of the intersection threshold r outward. For the generation of street and alley - level road networks, r is set to 50m.

6. The method for identifying potential street vector in historical urban areas based on axis extraction according to claim 1, characterized in that Step A specifically includes the following sub - steps: A1. Obtain map data from the open - source platform, determine the street and alley recognition range, classify the layers of the selected historical block texture DXF file, extract the plot boundary, street and alley centerline, and building contour information, and convert it into the Map file format; A2. Use the NetTopologySuite library in the Unity engine to read the imported DXF file, classify the geometric data therein, and convert it into the MAP file format; A3. Define a class named Map, which contains multiple attributes and collections for storing and managing different elements in the map. Each Map object includes the following content: Map name: used to identify the specific map; Road list: including roads at different levels, storing arterial roads, branch roads and streets and alleys respectively; Building list: including ordinary buildings and historical buildings, marked as non-passable areas; Area list: storing non-passable enclosed areas and water systems.

7. The method for identifying potential street vector in historical urban areas based on axis extraction according to claim 6, wherein In step A2, primitive deduplication and line data conversion are performed before import to ensure the smooth progress of subsequent calculation and analysis processes.

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